import torch

import torch.nn.functional as F
import torch.nn as nn
import math
import torchvision.models as models

class CNN_BE(nn.Module):
    def __init__(self,mid_planes):
        super().__init__()
        self.conv0 = nn.Conv2d(1, mid_planes, kernel_size=3, stride=1, padding=1,padding_mode='circular', bias=False)
        self.conv1 = nn.Conv2d(mid_planes,mid_planes,kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        # self.bn1 = nn.BatchNorm2d(mid_planes)
        self.conv2 = nn.Conv2d(mid_planes,mid_planes,kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        # self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        self.conv4 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        self.conv5 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        self.conv6 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        # self.conv7 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv8 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv9 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv10 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv11 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv12 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv13 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                        bias=False)
        # self.conv14 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=1, padding=1, padding_mode='circular',
        #                         bias=False)
        self.convF = nn.Conv2d(mid_planes,1,kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)

    def forward(self,x):
        residual = self.conv0(x)

        out = self.conv0(x)
        #resblock1
        out = self.conv1(out)
        out = torch.tanh(out)
        out = self.conv2(out)
        out = residual+out
        out = torch.tanh(out)
        #resblock2
        residual = out
        out = self.conv3(out)
        out = torch.tanh(out)
        out = self.conv4(out)
        out = residual + out
        out = torch.tanh(out)

        #resblock3
        residual = out
        out = self.conv5(out)
        out = torch.tanh(out)
        out = self.conv6(out)
        out = residual + out
        out = torch.tanh(out)
        #
        # # # # resblock4
        # residual = out
        # out = self.conv7(out)
        # out = torch.tanh(out)
        # out = self.conv8(out)
        # out = residual + out
        # out = torch.tanh(out)
        # #
        # # # resblock5
        # residual = out
        # out = self.conv9(out)
        # out = torch.tanh(out)
        # out = self.conv10(out)
        # out = residual + out
        # out = torch.tanh(out)
        #
        # # # resblock6
        # # residual = out
        # out = self.conv11(out)
        # out = torch.tanh(out)
        # out = self.conv12(out)
        # out = residual + out
        # out = torch.tanh(out)
        # #
        # # # resblock7
        # residual = out
        # out = self.conv13(out)
        # out = torch.tanh(out)
        # out = self.conv14(out)
        # out = residual + out
        # out = torch.tanh(out)
        # out = torch.tanh(out)
        out = self.convF(out)

        return out


class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, padding_mode='circular',bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, padding_mode='circular',bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)

class ResBlock_CNN(nn.Module):
    """This is the resblock used in u-net"""


    def __init__(self, in_channels, out_channels):
        super().__init__()
        #this is the initial layer, upsample the input channel to output channel
        self.upsample = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, padding_mode='circular',bias=False),
            # nn.BatchNorm2d(out_channels),
            nn.Tanh(),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, padding_mode='circular',bias=False)
        )

    def forward(self, x):
        identity = self.upsample(x)
        out = self.double_conv(x)
        out = torch.tanh(out+identity)
        return out


class Up(nn.Module):
    """Upscaling then double conv"""
    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = ResBlock_CNN(in_channels, out_channels)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.conv = ResBlock_CNN(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])

        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)




class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            ResBlock_CNN(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)



class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels,out_channels,kernel_size=3, stride=1, padding=1, padding_mode='circular',bias=False)

    def forward(self, x):
        return self.conv(x)


class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=False):
        super(UNet, self).__init__()
        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits


if __name__ == '__main__':

    x=torch.randn((1,1,50,50))
    model = UNet(1,1)
    print(model)
    print(x.size())
    print(model(x).size())
    print(x)
    print(model(x))








